MODELLING AS DECISION SUPPORT TOOL FOR SUSTAINABLE TRANSPORT SYSTEM PLANNING: CASE-STUDY OF RIGA

Transcription

1 This work was supported by Latvian state research program project The next generation of information and communication technologies (NexIT) ( ) MODELLING AS DECISION SUPPORT TOOL FOR SUSTAINABLE TRANSPORT SYSTEM PLANNING: CASE-STUDY OF RIGA D R. S C. I N G. M I H A I L S S A V R A S O V S Seminārs Modernās informācijas un komunikācijas tehnoloģijas transporta sistēmu vadībai

2 Introduction Outline Case-study 1: highway P133 and Ziemelu street intersection simulation and capacity estimation Case-study 2: pedestrian and transport flows analysis for pedestrian street creation in Riga Case-study 3: freight traffic flow research and rerouting from Riga city center Conclusions 2

3 Requirements to the modern transport system Transport key element of modern economics 3 Ecological requirements Safety Effectiveness Commission of European Communities, "White paper- European transport policy," Luxembourg, , 2011.

4 Sustainable development Sustainable development is a development that meets the needs of the present without compromising the ability of future generations to meet their own needs Transport sustainable development tools: use of ITS (Intelligent Transportation Systems) use of P&R (Park and Ride) optimization of existing transport infrastructure development of intermodality implementation of new transport infrastructure elements base on strict analysis of development consequences use of sound tax policy etc. 4

5 Model-based decision-making 5 E. Yurshevich, Methodology of decision-making support based on urban transportation system microscopic models repositories, doctoral thesis, Riga, TTI, 2013

6 Hierarchy of TS modelling approaches Microscopic models Each vehicle is moved through the network of transportation facilities on a split second by split second basis according to the physical characteristics of the vehicle (length, maximum acceleration rate, etc.) Mesoscopic models models simulate individual vehicles, but describe their activities and interactions based on the aggregate (macroscopic) relationships Macroscopic models simulate traffic flow, taking into consideration cumulative traffic stream characteristics (speed, flow, and density) and their relationships to each other 6

7 Case-study 1: highway P133 and Ziemelu street intersection simulation and capacity estimation 7 M I H A I L S S A V R A S O V S V L A D I M I R V E N T I R I N A Y A T S K I V

8 Highway P133 and Ziemelu street intersection simulation and capacity estimation Goal: to test different scenarios on P133 and Ziemelu street intersection capacity improvement 8 Tasks: to develop microsocpic model of the intersection; to test different development scenarios to estimate level of services (LOS) for crossroads according to HCM standard to produce 3D animation of the model

9 Research object 9 Ziemeļu street To city center To airport to Mārupe

10 Development plans Cargo and passenger growth 10 Territory development Expedition hall Business center

11 Input data (traffic flow intensity) 11

12 Input data (traffic light data) 12

13 Simulation model development PTV VISION VISSIM links and connectors 76 - conflict areas 5 traffic input point 2 public transport stops 1 public transport route 12 traffic lights > 100 3D objects 13

14 Particularities of the model Some directions are controlled by detectors 14

15 Scenarios Title Description 1 Scenario 1: P Intensity: Forecasted 2 Scenario 2: P+2J Intensity: Forecasted 2 lanes from Ziemeļu iela 3 Scenario 3: P+2J+L1 Intensity: Forecasted 2 lanes from Ziemeļu iela Fix traffic light cycle (1 st variant) 7 Scenario 7: P+2J+L2 Intensity: Forecasted 2 lanes from Ziemeļu iela Fix traffic light cycle (2nd variant) 11 Scenario 11: P175+2J+L3 15 Intensity: Forecasted (Marupe direction 175 v/h) 2 lanes from Ziemeļu iela Fix traffic light cycle (3rd variant)

16 Level of Service 16 LOS LOS Время задержки (с) Delay time (s) A 10 B > C > D >35 55 E > F >80

17 Simulation results 17 LOS Krustojums Virziens Braukšana 1 NB (Developing territory) EB (Airport) SB (Marupe) WB (City center) Left 2 Through Right 2 Scenāriji Total F F F F F E F F E D E Left 2 Through Right 1 Total C C C C C C D D D D D Left 2 Through Total F F F F F F E E E F E Left 2 Through Right 2 Total D D D D D D D D D D D Total F F F F E E E E E E E

18 Simulation results 18 Delay time, s

19 Simulation results 19 Average speed, km/h

20 3D animation 20

21 Case-study 2: pedestrian and transport flows analysis for pedestrian street creation in Riga 21 I R I N A Y A T S K I V E L E N A Y U R S H E V I C H M I H A I L S S A V R A S O V S

22 Goal: Pedestrian and transport flows analysis for pedestrian street creation in Riga city to test different development scenarios of the Terbatas street Tasks: to make a traffic counts survey to obtain information about current use of the street and around lying region; to estimate public transport loading level and to make public transport passenger questionnaire about origin, destination, travel goal and preferences; to develop and calibrate microscopic model, which describes current situations; to test different development scenarios; to forecast traffic flow redistribution and to estimate level of services (LOS) for crossroads according to HCM standard. 22

23 Traffic counts 23 Date and time: 29 September from 7:30 till 9:30 29 September from 16:30 till 18:30 Wednesday 30 September from 7:30 till 9:30 30 September 16:30 till 18:30 Thursday 150 TTI students (traffic counts) 10 students (data processing, analysis, visualization)

24 Object 37 Crossroads: 24 Brīvības iela Elizabetes iela Brīvības iela Dzirnavu iela Brīvības iela Blaumaņa iela Brīvības iela Lāčplēša iela Brīvības iela Ģertrūdes iela Brīvības iela Stabu iela Brīvības iela Bruņinieku iela Brīvības iela Matīsa iela Brīvības iela Tallinas iela Brīvības iela Cēsu iela Tērbatas iela Elizabetes iela Tērbatas iela Dzirnavu iela Tērbatas iela Blaumaņa iela Tērbatas iela Lāčplēša iela Tērbatas iela Ģertrūdes iela Tērbatas iela Stabu iela Tērbatas iela Bruņinieku iela Tērbatas iela Matīsa iela Tērbatas iela Tallinas iela Krišjāņa Barona iela Elizabetes iela Krišjāņa Barona iela Dzirnavu iela Krišjāņa Barona iela Blaumaņa iela Krišjāņa Barona iela Lāčplēša iela Krišjāņa Barona iela Ģertrūdes iela Krišjāņa Barona iela Stabu iela Krišjāņa Barona iela Bruņinieku iela Krišjāņa Barona iela Matīsa iela Krišjāņa Barona iela Tallinas iela Marijas iela Elizabetes iela Marijas iela Dzirnavu iela Aleksandra Čaka iela Blaumaņa iela Aleksandra Čaka iela Lāčplēša iela Aleksandra Čaka iela Ģertrūdes iela Aleksandra Čaka iela Stabu iela Aleksandra Čaka iela Bruņinieku iela Aleksandra Čaka iela Matīsa iela Aleksandra Čaka iela Tallinas iela

25 Traffic count results 25

26 Simulation Results (1/3) 26

27 Simulation Results (2/3) 27

28 Simulation Results (3/3) 28

29 Case-study 3: freight traffic flow research and rerouting from Riga city center 29 I R I N A Y A T S K I V M I H A I L S S A V R A S O V S I R I N A P T I C I N A I L Y A C I G R I N S N A D E Z H D A B O R O V I K O V A R I T A S T R O D E

30 Transport system 30 GDP 53%

31 Cargo turnover in Riga port 31

32 Cargo turnover by type 32

33 Average daily traffic flow intensity (2013) 33

34 Traffic counts 34 Date and time: 19 February from 10:00 till 12:00 19 February from 14:00 till 16:00 Wednesday 20 February from 10:00 till 12:00 20 February from 14:00 till 16:00 Thursday

35 Counted types of vehicles 35

36 Counting sites 19 counting sites: Vanšu tilts Akmens tilts Salu tilts Dienvidu tilts Maskavas iela Krasta iela 11. novembra krastmala Ziepniekkalna iela Mūkusalas iela Gustava Zemgale gatve Daugavgrīvas iela Raņķa dambis Slāvu iela Lubānas iela Hanzas iela Skanstes iela Vienības gatve Kārļa Ulmaņa gatve Viestura prospekts 36

37 Counting results (1/2) 37 Virziens: 5A / Maskavas iela (uz centru) Datums: (Transportlīdzekļu skaits) C4 C3 C2 C1 V

38 Counting results (2/2) 38 Virziens: 5A / Maskavas iela (uz centru) Datums: Transporta plūsmu struktura 3% 2% 0% 9% 86% V C1 C2 C3 C4

39 Total intensity

40 Total cargo flow intensity

41 Macromodel development nodes links 27 transport zones 2 OD matrix

42 Calibration and validation (1/2) 42

43 Calibration and validation R² = 0,

44 Scenario Changes in transport system Changes in traffic intensity 2014_B NO NO 2014_C1 Cargo flow restriction from Salu bridge to Hanzas iela NO 2014_C2 2014_C3 2014_VC1 2014_VC2 2014_VC3 Cargo flow restriction from Salu bridge to Hanzas iela and on Akmens bridge Cargo flow restriction from Salu bridge to Hanzas iela and on Akmens bridge and Sala bridge Transport flow restriction from Akmens bridge and Vanšu bridge Transport flow restriction from Turgeņeva street to Dzelzceļa bridge Transport flow restriction from Vanšu bridge Turgeņeva street NO NO All types of vehicles: - 5% All types of vehicles: - 5% All types of vehicles: - 5% 44

45 Project Austrumu maģistrāle 45

46 km/st Simulation results Scenārijs

47 Level of service (HCM) 47

48 Conclusions 48 Traffic flow simulation is a modern approach of TS quantitative analysis, which could be used for decision making Different types of models could be used in different level of decision making Use of model require prove of data validity, model validity, correctness of evaluation Use of standalone models is effective, but not for city authorities The important parts of DSS are: data collection procedures and technical implementation; database of the models; procedures of linkage different models issues related to data and model access

49 This work was supported by Latvian state research program project The next generation of information and communication technologies (NexIT) ( ) 49